On the Performance Degradation of Dominance-Based Evolutionary Algorithms in Many-Objective Optimization-Reference-Cited by-同舟云学术

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On the Performance Degradation of Dominance-Based Evolutionary Algorithms in Many-Objective Optimization

Published:2018-02 Issue:1 Volume:22 Page:19-31
ISSN:1089-778X
Container-title:IEEE Transactions on Evolutionary Computation
language:
Short-container-title:IEEE Trans. Evol. Computat.

Author:

Santos Thiago,Takahashi Ricardo H. C.^ORCID

Funder

Brazilian agency CNPq

Brazilian agency CAPES

Brazilian agency FAPEMIG

Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/4235/8272041/07727968.pdf?arnumber=7727968

Cited by 21 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. On the adaptation of reference sets using niching and pair-potential energy functions for multi-objective optimization;Swarm and Evolutionary Computation;2023-12

2. A Many-Objective Evolutionary Algorithm Based on Non-Linear Dominance;International Journal of Swarm Intelligence Research;2023-05-18

3. A general framework for enhancing relaxed Pareto dominance methods in evolutionary many-objective optimization;Natural Computing;2022-07-27

4. POSIMNET-R: An immunologic resilient approach to position routers in Industrial Wireless Sensor Networks;Expert Systems with Applications;2022-02

5. A Gradient-Based Search Method for Multi-objective Optimization Problems;Information Sciences;2021-11

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